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Alas, coordinated hate attacks, or raids, are becoming increasingly commononline. In a nutshell, these are perpetrated by a group of aggressors whoorganize and coordinate operations on a platform (e.g., 4chan) to targetvictims on another community (e.g., YouTube). In this paper, we focus onattributing raids to their source community, paving the way for moderationapproaches that take the context (and potentially the motivation) of an attackinto consideration. We present TUBERAIDER, an attribution system achieving over75% accuracy in detecting and attributing coordinated hate attacks on YouTubevideos. We instantiate it using links to YouTube videos shared on 4chan's /pol/board, r/The_Donald, and 16 Incels-related subreddits. We use a peak detectorto identify a rise in the comment activity of a YouTube video, which signalsthat an attack may be occurring. We then train a machine learning classifierbased on the community language (i.e., TF-IDF scores of relevant keywords) toperform the attribution. We test TUBERAIDER in the wild and present a few casestudies of actual aggression attacks identified by it to showcase itseffectiveness.

TUBERAIDER: Attributing Coordinated Hate Attacks on YouTube Videos to their Source Communities